Morphological detection and feature-based classification of cracked regions in ferrites

• Autorzy: Nieniewski M. , Chmielewski L. , Jóźwik A. , Skłodowski M.
• Konferencja: Euromech 406 colloquium on Image Processing Methods in Applied Mechanics (6-8.05.1999 ; Warsaw, Poland)
• Języki publikacji: EN

Abstrakty

EN

Automatic quality inspection of ferrite products is difficult as thier surfaces are dark and in many cases coverred with traces of grinding. A two-stage vision system for detection and measurement of crack regions was devised. In the first stage the regions with strong evidence for cracks are found using a morphological detector of irregular grightness changes with subsequent morphological reconstruction. In the second stage the feature-based k-Nearest Neighbors classifier analyzes the pixel indicated in the first stage. The classifier is optimized by using procedures of reclassification and replacement carried out on the reference set of pattern pixels to achieve a low error rate and a maximum speed of computation.

Słowa kluczowe

EN

morphological defect detection; surface defects; morphological reconstruction; defect classification; k--Neighbors classification; parallel classifier

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 1999
• Tom: Vol. 8, No. 4
• Strony: 699--712
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Nieniewski M.

autor

Chmielewski L.

autor

Jóźwik A.

autor

Skłodowski M.

• Institute of Fundamental Technological Research, PAS,